Regional structural and functional variations in the posteromedial cortex (PMC) have been found in both animals and humans, strongly suggesting the presence of subdivisions. However, there is no consensus on how to subdivide the human PMC. Here, we investigated the anatomical parcellation scheme and the connectivity pattern of each subdivision of the human PMC using diffusion tensor imaging data from 2 independent groups of volunteers. The parcellation analyses of the 2 datasets consistently demonstrated that the human PMC can be parcellated into 5 subregions. The dorsal portion of the PMC was subdivided into anterior, central, and posterior subregions, which participate in sensorimotor, associative, and visual functions. The ventral PMC contained a transitional region in the dorsal portion and a ventral subregion that is the core of the default mode network. The parcellation results for the human PMC and its anatomical connectivity patterns were further supported by evidence from the macaque PMC. Furthermore, functional connectivity analysis revealed that each subregion exhibited a specific pattern similar to that of its anatomical connectivity. The proposed parcellation scheme may facilitate the study of the human PMC at a subtler level and improve our understanding of its functions.

Discriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present serious challenges. In this study, a novel feature selection approach based on linear support vector machine with a forward-backward search strategy (SVM-FoBa) was developed and applied to structural and resting-state functional magnetic resonance imaging data collected from 21 BD, 25 MDD and 23 healthy controls. Discriminative features were drawn from both data modalities, with which the classification of BD and MDD achieved an accuracy of 92.1% (1,000 bootstrap resamples). Weight analysis of the selected features further revealed that the inferior frontal gyrus may characterize a central role in BD-MDD differentiation, in addition to the default mode network and the cerebellum. A modality-wise comparison also suggested that functional information outweighs anatomical by a large margin when classifying the two clinical disorders. This work validated the advantages of multimodal joint analysis and the effectiveness of SVM-FoBa, which has potential for use in identifying possible biomarkers for several mental disorders.

Men are more risk prone than women, but the underlying basis remains unclear. To investigate this question, we developed a trait-like measure of risk propensity which we correlated with resting-state functional connectivity to identify sex differences. Specifically, we used short- and long-range functional connectivity densities to identify associated brain regions and examined their functional connectivities in resting-state functional magnetic resonance imaging (fMRI) data collected from a large sample of healthy young volunteers. We found that men had a higher level of general risk propensity (GRP) than women. At the neural level, although they shared a common neural correlate of GRP in a network centered at the right inferior frontal gyrus, men and women differed in a network centered at the right secondary somatosensory cortex, which included the bilateral dorsal anterior/middle insular cortices and the dorsal anterior cingulate cortex. In addition, men and women differed in a local network centered at the left inferior orbitofrontal cortex. Most of the regions identified by this resting-state fMRI study have been previously implicated in risk processing when people make risky decisions. This study provides a new perspective on the brain-behavioral relationships in risky decision making and contributes to our understanding of sex differences in risk propensity.